A Comparison of the Composition of Selected Commercial Sandalwood Oils with the International Standard

Sandalwood oils are highly desired but expensive, and hence many counterfeit oils are sold in high street shops. The study aimed to determine the content of oils sold under the name sandalwood oil and then compare their chromatographic profile and α- and β santalol content with the requirements of ISO 3518:2002. Gas chromatography with mass spectrometry analysis found that none of the six tested “sandalwood” oils met the ISO standard, especially in terms of α-santalol content. Only one sample was found to contain both α- and β-santalol, characteristic of Santalum album. In three samples, valerianol, elemol, eudesmol isomers, and caryophyllene dominated, indicating the presence of Amyris balsamifera oil. Another two oil samples were found to be synthetic mixtures: benzyl benzoate predominating in one, and synthetic alcohols, such as javanol, polysantol and ebanol, in the other. The product label only gave correct information in three cases: one sample containing Santalum album oil and two samples containing Amyris balsamifera oil. The synthetic samples described as 100% natural essential oil from sandalwood are particularly dangerous and misleading to the consumer. Moreover, the toxicological properties of javanol, polysantol and ebanol, for example, are unknown.     

Characterization of Gasoline by Raman Spectroscopy with Chemometric Analysis

A rapid Raman spectroscopy protocol is reported to classify gasoline according to its distributor and to identify and quantify common adulterants. Gasoline from three distributors was collected from 19 stations in São Paulo, Brazil. Principal component analysis (PCA) showed specific clusters for each distributor, and partial least squares discriminant analysis (PLS-DA) correctly identified the origin of the samples. To evaluate the technique for the identification and quantification of the adulterants, authentic samples from each distributor were fortified at levels from 2.5 up to 25.0% (v/v) using ethanol, methanol, toluene, and turpentine to obtain 120 altered samples. PCA showed clear separation among the samples with the adulterants and PLS-DA precisely identified the adulterants (478 in 480 predictions by cross-validation), irrespective of the distributor and the concentration. One classification model was used to characterize all distributors. To quantify the adulterants, 36 multivariate calibration models were constructed using partial least squares (PLS), interval PLS, and PLS genetic algorithm for each distributor and for each adulterant. Cross-validation errors of less than 5.0% were obtained for all adulterants regardless of the distributor. Raman spectroscopy and multivariate analysis were shown to be powerful for rapid and inexpensive for the characterization of gasoline origin and the identification and quantification of common adulterants.     

超高效液相色谱-三重四极杆质谱法测定保健食品中30种非法添加壮阳类化合物

建立了超高效液相色谱-三重四极杆质谱测定保健食品中30种壮阳类化合物的方法。采用Waters CORTECS T3色谱柱(100 mm×2.1 mm,2.7μm),以0.1%(体积分数)甲酸水溶液-含0.1%(体积分数)甲酸的乙腈溶液为流动相,梯度洗脱,在电喷雾离子化正离子模式下,以多反应监测模式(MRM)检测。30种化合物在10~200μg/L范围内均呈良好的线性关系,相关系数均大于0.99;在0.1、0.2、1.0 mg/kg加标水平下的平均加标回收率为62.8%~135.2%,相对标准偏差均不大于14%(n=6);检出限为0.05 mg/kg,定量下限为0.1 mg/kg。该方法定性定量准确,操作简便,可为国家标准的建立提供参考。     

Quantification of Corn Adulteration in Wet and Dry-Processed Peaberry Ground Roasted Coffees by UV–Vis Spectroscopy and Chemometrics

In this present research, a spectroscopic method based on UV–Vis spectroscopy is utilized to quantify the level of corn adulteration in peaberry ground roasted coffee by chemometrics. Peaberry coffee with two types of bean processing of wet and dry-processed methods was used and intentionally adulterated by corn with a 10–50% level of adulteration. UV–Vis spectral data are obtained for aqueous samples in the range between 250 and 400 nm with a 1 nm interval. Three multivariate regression methods, including partial least squares regression (PLSR), multiple linear regression (MLR), and principal component regression (PCR), are used to predict the level of corn adulteration. The result shows that all individual regression models using individual wet and dry samples are better than that of global regression models using combined wet and dry samples. The best calibration model for individual wet and dry and combined samples is obtained for the PLSR model with a coefficient of determination in the range of 0.83–0.93 and RMSE below 6% (w/w) for calibration and validation. However, the error prediction in terms of RMSEP and bias were highly increased when the individual regression model was used to predict the level of corn adulteration with differences in the bean processing method. The obtained results demonstrate that the use of the global PLSR model is better in predicting the level of corn adulteration. The error prediction for this global model is acceptable with low RMSEP and bias for both individual and combined prediction samples. The obtained RPD_p and RER_p in prediction for the global PLSR model are more than two and five for individual and combined samples, respectively. The proposed method using UV–Vis spectroscopy with a global PLSR model can be applied to quantify the level of corn adulteration in peaberry ground roasted coffee with different bean processing methods.     

Mn的掺入对LaSrCo1-xMnxO4催化剂催化性能的影响

采用无定形聚合体分解法,在较低温度及空气气氛下合成了具有类钙钛矿型结构的LaSrCO1-xMnxO4(x=0.1,0.3,0.5)催化剂,以CO氧化为探针反应,运用XRD、BET、DTA、DTG、XPS等手段进行表征。结果表明:低于500℃,样品的前驱体分解为组成LaSrCo1-xMnxO4复合氧化物的物种,且650℃焙烧6h后,均形成类钙钛矿型结构;焙烧温度严重影响颗粒大小及比表面积;随着锰的掺入量增多,LaSrCo1-xMnxO4复合氧化物中Co2 的特征峰强度减弱;Mn的2p3/2结合能显示锰元素主要以Mn3 形式存在,锰的含量对Mn的2p3/2结合能的影响甚微;CO氧化反应的催化活性与LaSrCo1-xMnxO4复合氧化物的B位正三价离子含量及晶格氧的脱附量有密切联系,掺入适量的锰,有利于促进CO氧化活性的改善。     

Sugar Profiling of Honeys for Authentication and Detection of Adulterants Using High-Performance Thin Layer Chromatography

Honey adulteration, where a range of sugar syrups is used to increase bulk volume, is a common problem that has significant negative impacts on the honey industry, both economically and from a consumer confidence perspective. This paper investigates High-Performance Thin Layer Chromatography (HPTLC) for the authentication and detection of sugar adulterants in honey. The sugar composition of various Australian honeys (Manuka, Jarrah, Marri, Karri, Peppermint and White Gum) was first determined to illustrate the variance depending on the floral origin. Two of the honeys (Manuka and Jarrah) were then artificially adulterated with six different sugar syrups (rice, corn, golden, treacle, glucose and maple syrup). The findings demonstrate that HPTLC sugar profiles, in combination with organic extract profiles, can easily detect the sugar adulterants. As major sugars found in honey, the quantification of fructose and glucose, and their concentration ratio can be used to authenticate the honeys. Quantifications of sucrose and maltose can be used to identify the type of syrup adulterant, in particular when used in combination with HPTLC fingerprinting of the organic honey extracts.     

NATURAL RESOURCE MODELING: ALLOWING FOR INCONSTANT PROBABILITY OF DETECTION AND FREQUENCY MEASURES OF VIOLATION WITHIN DYNAMIC DETERRENCE FISHERY MODELS

Abstract. The dynamic deterrence model (DDM) represents an analytical framework widely used for analyzing compliance with fishery regulations aiming for sustainable fishery management. However, applications of the DDM have so far been limited to the case of constant probability of detection that assumes independence of the length of time to detection. This paper modifies the DDM to allow for more flexible and broader specification by introducing, for the first time, two important variables to the supply of offences function, namely evasion activity and enforcement efforts and accommodating inconstant probability of detection specifications. Determinants of probability of detection are specified and important potential extensions of the modified DDM are discussed in this paper. The relative magnitude of the effects of each of these determinants on probability of detection, however, remains an important empirical question that requires further investigation for prioritization of policy actions.     

MIP-SERS技术快速检测中药中非法添加的茶碱

建立了分子印迹技术(MIP)与表面增强拉曼光谱(SERS)联用对中药中非法添加的茶碱成分快速检测的方法。基于沉淀聚合方法合成MIP微球,对复杂中药基质中的茶碱进行简单分离,采用SERS对MIP中吸附的茶碱进行定性检测。实验利用SEM,FT-IR对MIP结构表征,考察了MIP的热力学、动力学和选择性吸附能力,以及掺杂成分的检出限。结果表明,MIP比NIP对茶碱具有更好的特异性吸附和选择性,该方法对茶碱的检出限低至0.1μg/L。方法用于5种止咳平喘类中药的检测,其中1种中药检出非法添加了茶碱成分。该法灵敏度高,特异性强,无需前处理,简单快速,实现了掺伪中药中茶碱的快速检测,有望进一步应用于其他复杂基质体系。     

单类分类方法结合光谱分析在食品真实性鉴别中的应用

近年来,假冒伪劣食品已日益成为广大消费者密切关注的问题,食品真实性评估是缓解这一问题、保护公众健康的有力手段。在仪器设备和样品处理的高要求下,现代检测技术通常需要大量时间和金钱的成本消耗,而如今食品掺假手段不断变换,花样日益翻新,使得这类检测技术存在一定的局限性。为促进食品安全质量监管的效率和水平提高,为监管工作提供有力的科学技术支撑和保障,需要寻求新型检测技术。光谱分析技术,以操作简单、快速无损的优势近年来被广泛应用,作为一种间接分析技术,结合数据统计学中的分类方法建立模型后更能有效进行真假鉴别。在分类方法中,由于现实生活中五花八门的掺假类型以及在真假样本数量差异大的情况下,常用的分类方法效果可能出现偏差。但单类分类方法（one-class classification）是一种只针对一类实例建模分析,以特定的置信水平固定目标样本类的边界,对新样本的类别进行判定的方法,利用这一特点能有效区分不同于真实样本的数据,大大减少了检测的工作量,在食品掺假检测应用领域有一定的发展潜力。对近年来模式识别中的分类方法——单类分类方法进行了综述。通过阐述光谱分析结合分类方法用于食品掺假检测的必要性,比较在同一情形下多类分类方法和单类分类方法的判别率,简介单类分类方法的特点,并重点介绍几种常见的单类分类方法如数据驱动的簇类独立软模式（DD-SIMCA）、单类偏最小二乘（OCPLS）、单类支持向量机（OCSVM）以及单类随机森林（OCRF）,论述单类分类方法在食品真实性鉴别中的应用,具体在食用油,乳制品,饮料,保健品,香辛料及谷物方面进行了阐述,还分析了当前单类分类方法存在的问题,最后对该技术的应用前景进行展望,为食品认证分析提供了一定的理论依据。